
import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
    from PIL import Image
else:




    class 			UpperCAmelCase_     :


      '''simple docstring'''







      @staticmethod
      def 				_lowercase				(					*UpperCamelCase__     :					Any     ,				**UpperCamelCase__     :					Dict       )     ->      Dict:







        """simple docstring"""






        pass

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			hashlib.mda(image.tobytes()   )
  return m.hexdigest()[:10]

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			np.array(A_   )
  __magic_name__								=			npimg.shape
  return {"hash": hashimage(A_   ), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  a__       			=						dict(
      (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()	) if MODEL_FOR_MASK_GENERATION_MAPPING else [])	)
  a__       			=						dict(
      (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()	) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])	)
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					List[str]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			MaskGenerationPipeline(model=UpperCamelCase__     ,				image_processor=UpperCamelCase__       )
    return image_segmenter, [
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
    ]
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Dict       )     ->      Union[str, Any]:







    """simple docstring"""






    pass
  @require_tf
  @unittest.skip("""Image segmentation not implemented in TF"""       )
  def 				_lowercase				(					self     :					List[str]       )     ->      Optional[Any]:







    """simple docstring"""






    pass
  @slow
  @require_torch
  def 				_lowercase				(					self     :					int       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			pipeline("""mask-generation"""     ,				model="""facebook/sam-vit-huge"""       )

    __magic_name__								=			image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""     ,				points_per_batch=256       )

    # Shortening by hashing
    __magic_name__								=			[]
    for i, o in enumerate(outputs["""masks"""]       ):
      new_outupt += [{"mask": mask_to_test_readable(UpperCamelCase__       ), "scores": outputs["scores"][i]}]

    # fmt: off
    self.assertEqual(
        nested_simplify(UpperCamelCase__     ,				decimals=4       )     ,				[
            {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0444},
            {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.021},
            {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0167},
            {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0132},
            {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0053},
            {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (480, 640)}, """scores""": 0.9967},
            {"""mask""": {"""hash""": """453c7844bd""", """shape""": (480, 640)}, """scores""": 0.993},
            {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (480, 640)}, """scores""": 0.9909},
            {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (480, 640)}, """scores""": 0.9879},
            {"""mask""": {"""hash""": """801064ff79""", """shape""": (480, 640)}, """scores""": 0.9834},
            {"""mask""": {"""hash""": """6172f276ef""", """shape""": (480, 640)}, """scores""": 0.9716},
            {"""mask""": {"""hash""": """b49e60e084""", """shape""": (480, 640)}, """scores""": 0.9612},
            {"""mask""": {"""hash""": """a811e775fd""", """shape""": (480, 640)}, """scores""": 0.9599},
            {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (480, 640)}, """scores""": 0.9552},
            {"""mask""": {"""hash""": """9d8257e080""", """shape""": (480, 640)}, """scores""": 0.9532},
            {"""mask""": {"""hash""": """32de6454a8""", """shape""": (480, 640)}, """scores""": 0.9516},
            {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (480, 640)}, """scores""": 0.9499},
            {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (480, 640)}, """scores""": 0.9483},
            {"""mask""": {"""hash""": """c290813fb9""", """shape""": (480, 640)}, """scores""": 0.9464},
            {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (480, 640)}, """scores""": 0.943},
            {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (480, 640)}, """scores""": 0.943},
            {"""mask""": {"""hash""": """c749b25868""", """shape""": (480, 640)}, """scores""": 0.9408},
            {"""mask""": {"""hash""": """efb6cab859""", """shape""": (480, 640)}, """scores""": 0.9335},
            {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (480, 640)}, """scores""": 0.9326},
            {"""mask""": {"""hash""": """788b798e24""", """shape""": (480, 640)}, """scores""": 0.9262},
            {"""mask""": {"""hash""": """abea804f0e""", """shape""": (480, 640)}, """scores""": 0.8999},
            {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (480, 640)}, """scores""": 0.8986},
            {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (480, 640)}, """scores""": 0.8984},
            {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (480, 640)}, """scores""": 0.8873},
            {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (480, 640)}, """scores""": 0.8871}
        ]     ,				)
    # fmt: on





  @require_torch
  @slow
  def 				_lowercase				(					self     :					List[str]       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			"""facebook/sam-vit-huge"""
    __magic_name__								=			pipeline("""mask-generation"""     ,				model=UpperCamelCase__       )

    __magic_name__								=			image_segmenter(
        """http://images.cocodataset.org/val2017/000000039769.jpg"""     ,				pred_iou_thresh=1     ,				points_per_batch=256       )

    # Shortening by hashing
    __magic_name__								=			[]
    for i, o in enumerate(outputs["""masks"""]       ):
      new_outupt += [{"mask": mask_to_test_readable(UpperCamelCase__       ), "scores": outputs["scores"][i]}]

    self.assertEqual(
        nested_simplify(UpperCamelCase__     ,				decimals=4       )     ,				[
            {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0444},
            {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0210},
            {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0167},
            {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0132},
            {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0053},
        ]     ,				)



import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
    import bsa
    from bsa import BeautifulSoup


__lowerCAmelCase     :	int       			=  logging.get_logger(__name__)




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  def __init__(					self     :					Union[str, Any]     ,				**UpperCamelCase__     :					Union[str, Any]       )     ->      Union[str, Any]:







    """simple docstring"""






    requires_backends(self     ,				["""bs4"""]       )
    super().__init__(**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]
    __magic_name__								=			element if element.name else element.parent
    for parent in child.parents:  # type: bs4.element.Tag
      __magic_name__								=			parent.find_all(child.name     ,				recursive=UpperCamelCase__       )
      xpath_tags.append(child.name       )
      xpath_subscripts.append(
          0 if 1 == len(UpperCamelCase__       ) else next(i for i, s in enumerate(UpperCamelCase__     ,				1       ) if s is child       )       )
      __magic_name__								=			parent
    xpath_tags.reverse()
    xpath_subscripts.reverse()
    return xpath_tags, xpath_subscripts
  def 				_lowercase				(					self     :					Union[str, Any]     ,				UpperCamelCase__     :					List[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			BeautifulSoup(UpperCamelCase__     ,				"""html.parser"""       )

    __magic_name__								=			[]
    __magic_name__								=			[]
    __magic_name__								=			[]

    for element in html_code.descendants:
      if type(UpperCamelCase__       ) == bsa.element.NavigableString:
        if type(element.parent       ) != bsa.element.Tag:
          continue

        __magic_name__								=			html.unescape(UpperCamelCase__       ).strip()
        if not text_in_this_tag:
          continue

        all_doc_strings.append(UpperCamelCase__       )

        __magic_name__	,       __magic_name__								=			self.xpath_soup(UpperCamelCase__       )
        stringaxtag_seq.append(UpperCamelCase__       )
        stringaxsubs_seq.append(UpperCamelCase__       )

    if len(UpperCamelCase__       ) != len(UpperCamelCase__       ):
      raise ValueError("""Number of doc strings and xtags does not correspond"""       )
    if len(UpperCamelCase__       ) != len(UpperCamelCase__       ):
      raise ValueError("""Number of doc strings and xsubs does not correspond"""       )

    return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
  def 				_lowercase				(					self     :					List[Any]     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					List[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			""""""
    for tagname, subs in zip(UpperCamelCase__     ,				UpperCamelCase__       ):
      xpath += F'''/{tagname}'''
      if subs != 0:
        xpath += F'''[{subs}]'''
    return xpath





  def __call__(					self     :					str     ,				UpperCamelCase__     :					Tuple       )     ->      BatchFeature:







    """simple docstring"""






    __magic_name__								=			False

    # Check that strings has a valid type
    if isinstance(UpperCamelCase__     ,				UpperCamelCase__       ):
      __magic_name__								=			True
    elif isinstance(UpperCamelCase__     ,				(list, tuple)       ):
      if len(UpperCamelCase__       ) == 0 or isinstance(html_strings[0]     ,				UpperCamelCase__       ):
        __magic_name__								=			True

    if not valid_strings:
      raise ValueError(
          """HTML strings must of type `str`, `List[str]` (batch of examples), """
          F'''but is of type {type(UpperCamelCase__       )}.'''       )

    __magic_name__								=			bool(isinstance(UpperCamelCase__     ,				(list, tuple)       ) and (isinstance(html_strings[0]     ,				UpperCamelCase__       ))       )

    if not is_batched:
      __magic_name__								=			[html_strings]

    # Get nodes + xpaths
    __magic_name__								=			[]
    __magic_name__								=			[]
    for html_string in html_strings:
      __magic_name__	,       __magic_name__	,       __magic_name__								=			self.get_three_from_single(UpperCamelCase__       )
      nodes.append(UpperCamelCase__       )
      __magic_name__								=			[]
      for node, tag_list, sub_list in zip(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       ):
        __magic_name__								=			self.construct_xpath(UpperCamelCase__     ,				UpperCamelCase__       )
        xpath_strings.append(UpperCamelCase__       )
      xpaths.append(UpperCamelCase__       )

    # return as Dict
    __magic_name__								=			{"""nodes""": nodes, """xpaths""": xpaths}
    __magic_name__								=			BatchFeature(data=UpperCamelCase__     ,				tensor_type=UpperCamelCase__       )

    return encoded_inputs

